Method and device of inverse tone mapping and electronic device

ABSTRACT

Embodiments of the present application provide a method and a device of inverse tone mapping and an electronic device. The method includes: obtaining one or more low dynamic range images; performing a decomposition operation to the low dynamic range image to acquire a detail layer and a basic layer of the low dynamic range image; restoring the detail layer and the basic layer by using a predetermined first restoration network and a second restoration network to acquire restored detail layer and basic layer; and adjusting the restored detail layer and basic layer by using a predetermined fusion network to acquire an adjusted high dynamic range image. With the technical solution of the present application, the conversion from a low dynamic range image to a high dynamic range image can be more robustly completed without complicated parameter settings.

CROSS REFERENCE TO RELATED APPLICATION

This Application is a Section 371 National Stage Application ofInternational Application No. PCT/CN2019/091874, filed Jun. 19, 2019,and claims priority to Chinese Patent Application No. CN201910499995.3,filed Jun. 10, 2019, entitled “a method and a device of inverse tonemapping and an electronic device”, the content of which is incorporatedherein by reference.

TECHNICAL FIELD

The present application relates to the technical field of digital imageprocessing, and more particularly to a method and a device of inversetone mapping and an electronic device.

BACKGROUND

In the field of digital image processing, if an ordinary image is to bedisplayed on a High Dynamic Range (HDR) display device, it cannotprovide sufficient accuracy, so it is necessary to restore HDRinformation from the ordinary image. This process is called inverse tonemapping. Taking the application of inverse tone mapping technology in 4KTV technology as an example, since most media resources are still storedin a low dynamic range, the inverse tone mapping technology being usedto convert media resources from low dynamic range to high dynamic rangeis an important part of 4K TV technology.

In the prior art, a parametric model is proposed to complete theconversion of low dynamic range images to high dynamic range imagesthrough the parametric model. This method is mainly to expand thebrightness in order to display good vision effect on the high dynamicrange display. However, the existing method cannot completely restorelost information in the low dynamic range image, and requirescomplicated parameter settings. In addition, the current inverse tonemapping methods are mostly for high-quality low dynamic range images.However, in practice, most of media resources are stored in a lossycompressed format during transmission, which will cause compressionartifacts, and the compression artifacts have a serious effect on theresults of inverse tone mapping. Based on the existing technology, thereis a need to provide an inverse tone mapping scheme that can robustlycomplete the conversion of low dynamic range images to high dynamicrange images.

SUMMARY

In view of above, an object of the present application is to provide amethod and a device of inverse tone mapping, and electronic device tosolve the problem of poor conversion effect of a low dynamic range imageto a high dynamic range image existing in the prior art.

In order to solve the above technical problems, the embodiments of thepresent application are implemented as follows:

Embodiments of the present application provide a method of inverse tonemapping, the method including steps of:

acquiring one or more low dynamic range images;

performing a decomposition operation to the low dynamic range image toacquire a detail layer and a basic layer of the low dynamic range image;

restoring the detail layer and the basic layer by using a predeterminedfirst restoration network and a second restoration network to acquirerestored detail layer and basic layer; and

adjusting the restored detail layer and basic layer by using apredetermined fusion network to acquire an adjusted high dynamic rangeimage.

Optionally, the step of acquiring one or more low dynamic range imagesincludes step of:

compressing an original image to acquire a compressed low dynamic rangeimage.

Optionally, the step of performing a decomposition operation to the lowdynamic range image to acquire a detail layer and a basic layer of thelow dynamic range image comprises step of:

performing a decomposition operation to the low dynamic range imagebased on the Retinex theory to acquire the detail layer and the basiclayer of the low dynamic range image.

Optionally, the step of performing a decomposition operation to the lowdynamic range image based on the Retinex theory to acquire the detaillayer and the basic layer of the low dynamic range image specificallyincludes step of:

performing edge-preserving filtering to the low dynamic range image, andusing the image acquired after the edge-preserving filtering as thebasic layer of the low dynamic range image; and calculating a differencebetween the low dynamic range image and the basic layer image, and theimage acquired after the difference being used as the detail layer ofthe low dynamic range image.

Optionally, the first restoration network is a residual network and thesecond first restoration network is a U-Net network; the step ofrestoring the detail layer and the basic layer by using a predeterminedfirst restoration network and a predetermined second restoration networkincludes step of:

restoring the detail layer by using the residual network and restoringthe basic layer by using the U-Net network.

Optionally, the detail layer contains high frequency components andcompression artifacts of the low dynamic range image, and the basiclayer contains low frequency components of the low dynamic range image;the step of restoring the detail layer by using the residual network andrestoring the basic layer by using the U-Net network includes step of:

restoring the high frequency components by using the residual network toremove the compression artifacts, and restoring the low frequencycomponents by using the U-Net network.

Optionally, the high frequency components include edges and contours,and the low frequency components include color information andstructural information.

Optionally, the fusion network uses a residual network.

Embodiments of the present application further provide a device ofinverse tone mapping, including:

an acquisition module, configured to acquire one or more low dynamicrange images;

a decomposition module, configured to perform a decomposition operationto the low dynamic range image to acquire a detail layer and a basiclayer of the low dynamic range image;

a restoring module, configured to respectively recover the detail layerand the basic layer by using a predetermined first restoration networkand a predetermined second restoration network to acquire restoreddetail layer and basic layer;

an adjustment module, configured to adjust the restored detail layer andbasic layer by using a predetermined fusion network to acquire anadjusted high dynamic range image.

Optionally, the acquisition module specifically configured to compressan original image to acquire a compressed low dynamic range image.

Optionally, the decomposition module specifically configured to performa decomposition operation to the low dynamic range image based on theRetinex theory to acquire the detail layer and the basic layer of thelow dynamic range image.

Optionally, the decomposition module further configured to performedge-preserving filtering to the low dynamic range image, and to use theimage acquired after the edge-preserving filtering as the basic layer ofthe low dynamic range image; and to calculate a difference between thelow dynamic range image and the basic layer image, and the imageacquired after the difference being used as the detail layer of the lowdynamic range image.

Optionally, the first restoration network is a residual network and thesecond first restoration network is a U-Net network; and the restoringmodule specifically configured to restore the detail layer by using theresidual network and to restore the basic layer by using the U-Netnetwork.

Optionally, the detail layer contains high frequency components andcompression artifacts of the low dynamic range image, and the basiclayer contains low frequency components of the low dynamic range image;and the restoring module further configured to restore the highfrequency components by using the residual network to remove thecompression artifacts, and to restore the low frequency components byusing the U-Net network.

Optionally, the high frequency components include edges and contours,and the low frequency components include color information andstructural information.

Embodiments of the present application further provide an electronicdevice, comprising: a storage device, a processor, and a computerprogram stored on the memory and executable on the processor, whereinthe processor implements the method of above mentioned when theprocessor executes the program.

The above at least one technical solution adopted in the embodiments ofthe present application can achieve the following beneficial effects: inthe present application, by acquiring one or more low dynamic rangeimages, and performing a decomposition operation to the low dynamicrange image to acquire a detail layer and a basic layer of the lowdynamic range image; and then restoring the detail layer and the basiclayer by using a predetermined first restoration network and apredetermined second restoration network to acquire restored detaillayer and basic layer; and then adjusting the restored detail layer andbasic layer by using a predetermined fusion network to acquire anadjusted high dynamic range image. With the technical solution of thepresent application, the conversion from a low dynamic range image to ahigh dynamic range image can be more robustly completed withoutcomplicated parameter settings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the embodiments of the present invention moreclearly, a brief introduction regarding the accompanying drawings thatneed to be used for describing the embodiments of the present inventionor the prior art is given below; it is obvious that the accompanyingdrawings described as follows are only some embodiments of the presentinvention, for those skilled in the art, other drawings can also beobtained according to the current drawings on the premise of paying nocreative labor.

FIG. 1 is a schematic flowchart of a method of inverse tone mappingprovided by an embodiment of the present application;

FIG. 2 is a structural schematic view of a residual network provided byan embodiment of the present application;

FIG. 3 is a structural schematic view of a U-net network provided by anembodiment of the present application;

FIG. 4 is a structural schematic view of fusion network provided by anembodiment of the present application; and

FIG. 5 is a structural schematic view of a device of inverse tonemapping provided by an embodiment of the present application.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand thetechnical solutions in the present application, the technical solutionsin the embodiments of the present application will be described clearlyand completely in conjunction with the drawings in the embodiments ofthe present application. Obviously, the described embodiments are only apart of the embodiments of the present application, but not all theembodiments. Based on the embodiments of the present application, allother embodiments acquired by those of ordinary skill in the art withoutcreative work shall fall within the scope of protection of the presentapplication.

In practical applications, since the content of Standard Dynamic Range(SDR) cannot be displayed on an HDR display device with sufficientaccuracy, and the current HDR content is in short supply, there is anurgent need for a technology that can convert SDR content to HDRcontent, inverse tone mapping technology came into being. At present,most of the media resources (such as images) are still stored in a lowdynamic range, and in order to facilitate the storage and transmissionof images, ordinary images need to be stored in a lossy compressedformat, which also introduces compression artifacts, such as blockartifacts, band artifacts and ringing effects.

Most of methods of the current inverse tone mapping are aimed athigh-quality low dynamic range images, that is, the lossy compressed oruncompressed low dynamic range images. The traditional method of inversetone mapping is usually to propose a parameter model, through theparameter model to complete the conversion of low dynamic range imagesto high dynamic range images. This traditional method is mainly toexpand the brightness in order to display a good visual effect on a highdynamic range display. However, this method has many drawbacks, itcannot fully restore the information lost in the low dynamic rangeimage, and it also requires complicated parameter settings, which isalso very difficult for ordinary users. In addition, ordinary images arestored in a lossy compressed format during transmission, which resultsin the generation of compression artifacts, which have a serious impacton the results of inverse tone mapping. If the compression artifacts arefirst removed and then using the inverse tone mapping, the result willbe too blurry. If removing the compression artifacts on the basis of theinverse tone mapping, the artifacts will be enhanced due to the inversetone mapping and difficult to remove.

Therefore, for low quality and low dynamic range images, that is, thelow dynamic range images stored in a lossy compression format, it isnecessary to provide an inverse tone mapping scheme that can restore theinformation lost during compression of the low dynamic range image andsimultaneously remove compression artifacts, and more robustly completethe conversion of low dynamic range images to high dynamic range images.The following embodiments of the present application may be performed ona low dynamic range image as a processing object, especially on a lossycompressed low dynamic range image (that is, a low quality and lowdynamic range image) as a processing object. Of course, using the lossycompressed low dynamic range image as the processing object is only anapplication scenario of the embodiment of the present application, andcannot constitute a limitation on the application range of theembodiment of the present application.

FIG. 1 is a schematic flowchart of a method of inverse tone mappingprovided by an embodiment of the present application. The method mayspecifically include the following steps:

Step S110, acquiring one or more low dynamic range images.

In one or more embodiments of the present application, the low dynamicrange image is a processing object of inverse tone mapping, andacquiring one or more low dynamic range images may be regarded asacquiring one or more images to be processed. According to theforegoing, the image to be processed in the embodiment of the presentapplication may be any low dynamic range image, including but notlimited to: uncompressed low dynamic range image, lossless compressedlow dynamic range image and lossy compressed low dynamic range image.Considering that most existing media resources are stored as lossycompressed images in order to facilitate storage and transmission, andthe lossy compression method causes the generation of compressionartifacts, so compared with other low dynamic range images, the lossycompressed low dynamic range images also need to consider the impact ofcompression artifacts on inverse tone mapping.

In a specific embodiment, the compressed low dynamic range image may beacquired by compressing the original images, where the compression maybe lossy compression, and the original images may include uncompressedlow dynamic range images, lossless compression low dynamic range imagesor high dynamic range images, and so on. Preferably, the followingembodiments of the present application are based on lossy compressed lowdynamic range images, that is, regarding to the low quality and lowdynamic range images as images to be processed.

It is noted that, in the embodiment of the present application, the lowdynamic range image can be considered as an image with a pixel valuebetween 0-255, and the high dynamic range image can be considered as animage with a pixel value between 0-16×10⁵.

Step S120, performing a decomposition operation to the low dynamic rangeimage to acquire a detail layer and a basic layer of the low dynamicrange image.

In one or more embodiments of the present application, continuing thecontent of the above embodiments, after acquiring a low quality and lowdynamic range image through compression processing, a decompositionoperation may be performed on the acquired low dynamic range image toacquire the detail layer and the basic layer of the low dynamic rangeimage. In the embodiments of the present application, the followingmethods may be used to perform decomposition operations on low dynamicrange images, and the method may include the following processes:

Performing a decomposition operation to the low dynamic range imagebased on the Retinex theory to acquire the detail layer and the basiclayer of the low dynamic range image. Specifically, performingedge-preserving filtering to the low dynamic range image, and using theimage obtained after the edge-preserving filtering as the basic layer ofthe low dynamic range image; and calculating a difference between thelow dynamic range image and the basic layer image, and the imageobtained after the difference being used as the detail layer of the lowdynamic range image.

The Retinex theory is a theory widely used in digital image processing,it believes that digital images can be decomposed into detail layers andbasic layers, and the two are independent of each other.

Further, considering that compression artifacts are caused by excessivecompression of the high frequency components, which mainly exist in thehigh frequency components, the low dynamic range image is decomposedinto high frequency and low frequency parts by using the Retinex theory,that is, decomposed into the detail layer and the basic layer; amongthem, the detail layer contains the high frequency components andcompression artifacts of the low dynamic range image, and the basiclayer contains the low frequency components of the low dynamic rangeimage.

Step S130, restoring the detail layer and the basic layer by using apredetermined first restoration network and a predetermined secondrestoration network to acquire restored detail layer and basic layer.

In one or more embodiments of the present application, since the detaillayer contains high frequency components and compression artifacts ofthe low dynamic range image, the high frequency components may includetexture information of the low dynamic range image, such as edges andcontours, and so on. Therefore, the detail layer needs to ensure theintegrity of its structure, reduce the loss of information, and avoiddown-sampling, therefore, a residual network with a constant feature mapsize can be used as the restoration network for the detail layer.

For the basic layer, it contains the low frequency components of the lowdynamic range image, and the low frequency components contain a lot ofcolor information and structural information (such as the shape of theobject), etc. This part of information being used to restore theover-exposed area, under-exposed area and color mapping has an importantrole. In order to extract sufficient features, it is necessary to usemulti-scale information to restore the object structure, so U-Netnetwork can be used as the restoration network of the basic layer.

Further, restoring the detail layer by using the residual network, andrestoring the basic layer by using the U-Net network; herein, the firstrestoration network is the residual network and the second restorationnetwork is the U-Net network.

According to the content of the foregoing embodiment, the detail layercontains high frequency components and compression artifacts of the lowdynamic range image, and the basic layer contains low frequencycomponents of the low dynamic range image; therefore, the residual layeris used to restore the detail layer and the U-Net network is used torestore the basic layer, which can include the following:

restoring the high frequency components by using the residual network toremove the compression artifacts, and restoring the low frequencycomponents by using the U-Net network.

The structure of the residual network and the U-Net network will bedescribed in detail below with reference to the drawings. As shown inFIGS. 2 to 3 , which show structural schematic views of the residualnetwork and the U-Net network provided by the embodiments of the presentapplication, and includes the following:

The residual network includes one or more convolution layers on bothsides and multiple residual blocks in the middle, and each of theresidual blocks contains a first convolution layer, a second activationlayer, and a third convolution layer and a fourth activation layerarranged in sequence; among then, before the fourth activation layerfurther includes: performing an addition operation on an input image ofthe residual block and an output image of the third convolution layer.

Further, in an embodiment of the present application, the residualnetwork may include 2 convolution layers on the front and rear sides and16 residual blocks in the middle. The activation layer in the residualblock uses the SELU activation function. The size of the convolutionkernel of each convolution layer in the residual network is 3*3, and thestep size is 1, except that the number of feature channels of the lastconvolution layer (that is, the rightmost convolution layer in FIG. 2 )is 3, the number of feature channels of the remaining layers are 64. Inpractical applications, in order to keep the size of the feature mapunchanged, edge filling can be done in a mirror-symmetrical manner.

The U-Net network includes multiple convolution blocks and deconvolutionblocks. The convolution blocks are located in front of the deconvolutionblocks. The convolution block includes a convolution layer, anactivation layer, and a convolution layer and an activation layerarranged in sequence; in order to avoid chessboard artifacts, in thedeconvolution block, first up-sampling to expand the resolution of thefeature map, and then performing the convolution operation. Thedeconvolution block contains an up-sampling layer and a convolutionlayer and an activation layer arranged in sequence. At the same time, inorder to accelerate the convergence speed, batch normalizationoperations are added to each layer, and in order to make full use of lowlevel features, there are jump links between the corresponding up anddown sampling layers

Further, in an embodiment of the present application, the U-Net networkincludes 5 convolution blocks and 4 deconvolution blocks, that is, theU-Net network may include a total of 9 layers; where each of the firstfour convolution blocks contains a convolution layer with a size of 1*1and a step size of 1, an activation layer, a convolution layer with asize of 3*3 and a step size of 2 and an activation layer arranged insequence, the number of feature channels of the first four convolutionblocks are 64, 128, 256, and 512 respectively; the fifth convolutionblock contains a convolution layer with a size of 3*3 and a step size of1, an activation layer, a convolution layer with a size of 3*3 and astep size of 1 and an activation layer arranged in sequence, and thenumber of feature channels of the fifth convolution block is 1024. Eachof the deconvolution blocks contain an up-sampling layer, a convolutionlayer with a size of 3*3 and a step size of 1 and an activation layerarranged in sequence, and the number of feature channel of thedeconvolution blocks are 512, 256, 128, 64, 3, respectively.

In the embodiment of the present application, the up-sampling adoptstwo-line up-sampling, and the resolution of the feature map can beenlarged through the up-sampling.

Step S140, adjusting the restored detail layer and basic layer by usinga predetermined fusion network to acquire an adjusted high dynamic rangeimage.

Continuing the above embodiment, after by using two differentrestoration networks to restore the high frequency components and thelow frequency components respectively, the restored detail layer and thebasic layer can be acquired, and finally the predetermined fusionnetwork is further used to adjust the restored detail layer and thebasic layer to acquire the final restored high dynamic range image.

Further, the fusion network may use a residual network, and thestructure of the fusion network is similar to the structure of the firstrestoration network (that is, the detail layer restoration network), butin practical applications, the fusion network may contain only 8residual blocks. Refer to FIG. 4 , which shows a structural schematicview of a fusion network provided by an embodiment of the presentapplication. Since the fusion network and the first restoration networkboth use the residual network, and the structures of the two aresimilar, the structure of the fusion network will not be repeatedherein. For the structure of the fusion network, can refer to the abovedescription of the structure of the first restoration network.

Based on the above embodiments of the present application, consideringthat compression artifacts are caused by excessive compression of highfrequency components, which mainly exist in the high frequencycomponents, therefore, the low dynamic range image is decomposed intohigh-frequency components (ie, the detail layer) and the low-frequencypart (ie, the basic layer) by the Retinex theory. Since the detail layercontains high frequency components of the image (such as edges andcontours) and compression artifacts, it is mainly responsible for edgeretention and removal of compression artifacts; while the basic layercontains low frequency components of the image (such as colorinformation and structural information), it is mainly responsible forthe recovery of overexposure and underexposure missing information andcolor mapping, so for the above two different component information, twodifferent networks are used to restore the detail layer and the basiclayer, and finally use a fusion network is used to further adjustresults of the first two networks to achieve the restoration of highdynamic range image. The present application can not only restore theinformation lost in the low dynamic range image, but also take intoaccount the restoration of different missing information, such as:overexposure areas, underexposure areas, color information and objectstructure, etc., and can also remove compression artifacts at the sametime, and the conversion of compressed low dynamic range images to highdynamic range images is more robustly completed.

Based on the same idea, an embodiment of the present application furtherprovides a device of inverse tone mapping. As shown in FIG. 5 , a deviceof inverse tone mapping is provided according to an embodiment of thepresent application. The device 500 mainly includes: an acquisitionmodule 501, configured to acquire one or more low dynamic range images;

a decomposition module 502, configured to perform a decompositionoperation to the low dynamic range image to acquire a detail layer and abasic layer of the low dynamic range image;

a restoring module 503, configured to respectively recover the detaillayer and the basic layer by using a predetermined first restorationnetwork and a predetermined second restoration network to acquirerestored detail layer and basic layer;

an adjustment module 504, configured to adjust the restored detail layerand basic layer by using a predetermined fusion network to acquire anadjusted high dynamic range image.

According to an embodiment of the present application, in the device,the acquisition module 501 specifically configured to compress anoriginal image to acquire a compressed low dynamic range image.

According to an embodiment of the present application, in the device,the decomposition module 502 specifically configured to perform adecomposition operation to the low dynamic range image based on theRetinex theory to acquire the detail layer and the basic layer of thelow dynamic range image.

According to an embodiment of the present application, in the device,the decomposition module 502 further configured to performedge-preserving filtering to the low dynamic range image, and to use theimage obtained after the edge-preserving filtering as the basic layer ofthe low dynamic range image; and to calculate a difference between thelow dynamic range image and the basic layer image, and the imageobtained after the difference being used as the detail layer of the lowdynamic range image.

According to an embodiment of the present application, in the device,the first restoration network is a residual network and the second firstrestoration network is a U-Net network; and the restoring module 503specifically configured to restore the detail layer by using theresidual network and to restore the basic layer by using the U-Netnetwork.

According to an embodiment of the present application, in the device,the detail layer contains high frequency components and compressionartifacts of the low dynamic range image, and the basic layer containslow frequency components of the low dynamic range image; and therestoring module 503 further configured to restore the high frequencycomponents by using the residual network to remove the compressionartifacts, and to restore the low frequency components by using theU-Net network.

According to an embodiment of the present application, in the device,the high frequency components comprise edges and contours, and the lowfrequency components comprise color information and structuralinformation.

Embodiments of the present application also provide an electronicdevice, including a storage device, a processor, and a computer programstored on the storage device and executable on the processor. When theprocessor executes the program, the above method of inverse tone mappingis implemented.

The foregoing describes specific embodiments of the present application.Other embodiments are within the scope of the following claims. In somecases, the actions or steps recited in the claims may be performed in adifferent order than in the embodiments and still achieve the desiredresults. In addition, the processes depicted in the drawings do notnecessarily require the particular order shown or sequential order toachieve the desired results. In some embodiments, multitasking andparallel processing are also possible or may be advantageous.

The embodiments in the present application are described in aprogressive manner. The same or similar parts between the embodimentscan be referred to each other, and each embodiment focuses on thedifferences from other embodiments. In particular, for the embodimentsof the device and the electronic device, since they are basicallysimilar to the method embodiments, the description is relatively simple.For the related parts, please refer to the description of the methodembodiments.

The device, the electronic device and the method provided in theembodiments of the present application correspond to each other.Therefore, the device and the electronic device also have beneficialtechnical effects similar to the corresponding method. Since thebeneficial technical effects of the method have been described in detailabove, therefore, the beneficial technical effects of the correspondingdevices and electronic equipment will not be described in detail herein.

The present application is described with reference to flowcharts and/orblock diagrams of methods, devices (systems), and computer programproducts according to embodiments of the present application. It shouldbe understood that each flow and/or block in the flowchart and/or blockdiagram and a combination of the flow and/or block in the flowchartand/or block diagram can be implemented by computer programinstructions. These computer program instructions can be provided to theprocessor of a general-purpose computer, special-purpose computer,embedded processing machine, or other programmable data processingdevice to produce a machine that enables the generation of instructionsexecuted by the processor of the computer or other programmable dataprocessing device A device for realizing the functions specified in oneblock or multiple blocks of one flow or multiple flows of a flowchartand/or one block or multiple blocks of a block diagram.

It should also be noted that the terms “include”, “contain” or any othervariant thereof are intended to cover non-exclusive inclusion, so that aprocess, method, commodity or device that includes a series of elementsincludes not only those elements, but also includes other elements notexplicitly listed, or include elements inherent to this process, method,commodity, or equipment. Without more restrictions, the element definedby the sentence “include one . . . ” does not exclude that there areother identical elements in the process, method, commodity or equipmentthat includes the element.

This description can be described in the general context ofcomputer-executable instructions executed by a computer, such as aprogram module. Generally, program modules include routines, programs,objects, components, data structures, etc. that perform specific tasksor implement specific abstract data types. The description may also bepracticed in distributed computing environments in which tasks areperformed by remote processing devices connected through a communicationnetwork. In a distributed computing environment, program modules may belocated in local and remote computer storage media including storagedevices.

The above description of the disclosed embodiments enables those skilledin the art to implement or use the present application. Variousmodifications to these embodiments will be apparent to those skilled inthe art, and the general principles defined herein can be implemented inother embodiments without departing from the spirit and scope of thepresent application. Therefore, the present application will not belimited to the embodiments shown herein, but should conform to thewidest scope consistent with the principles and novel features disclosedin the present application.

What is claimed is:
 1. A method of inverse tone mapping, comprisingsteps of: acquiring one or more low dynamic range images; performing adecomposition operation to the one or more low dynamic range images toacquire a detail layer and a basic layer of the low dynamic range image;restoring the detail layer and the basic layer by using a predeterminedfirst restoration network and a predetermined second restoration networkto acquire restored detail layer and basic layer, respectively; andadjusting the restored detail layer and basic layer by using apredetermined fusion network to acquire an adjusted high dynamic rangeimage; wherein the first restoration network is a residual network andthe second first restoration network is a U-Net network; wherein theresidual network comprises one or more convolution layers on both sidesand multiple residual blocks in the middle, and each of the residualblocks contains a first convolution layer, a second activation layer,and a third convolution layer and a fourth activation layer arranged insequence; and before the fourth activation layer further comprises:performing an addition operation on an input image of the residual blockand an output image of the third convolution layer; and wherein theU-Net network comprises multiple convolution blocks and deconvolutionblocks, the multiple convolution blocks are located in front of themultiple deconvolution blocks, and each of the multiple convolutionblock comprises a convolution layer, an activation layer, and aconvolution layer and an activation layer arranged in sequence, and ineach of the multiple deconvolution blocks, first up-sampling to expandthe resolution of the a feature map, and then performing the aconvolution operation; each of the multiple deconvolution blockscontains an up- sampling layer and a convolution layer and an activationlayer arranged in sequence.
 2. The method according to claim 1, whereinthe step of acquiring one or more low dynamic range images comprisesstep of: compressing an original image to acquire a compressed lowdynamic range image.
 3. The method according to claim 2, wherein thestep of performing a decomposition operation to the low dynamic rangeimage to acquire a detail layer and a basic layer of the low dynamicrange image comprises step of: performing a decomposition operation tothe low dynamic range image based on the Retinex theory to acquire thedetail layer and the basic layer of the low dynamic range image.
 4. Themethod according to claim 3, wherein the step of performing adecomposition operation to the low dynamic range image based on theRetinex theory to acquire the detail layer and the basic layer of thelow dynamic range image specifically comprises step of: performingedge-preserving filtering to the low dynamic range image, and using theimage acquired after the edge-preserving filtering as the basic layer ofthe low dynamic range image; and calculating a difference between thelow dynamic range image and the basic layer image, and the imageacquired after the difference being used as the detail layer of the lowdynamic range image.
 5. The method according to claim 1, wherein thestep of restoring the detail layer and the basic layer by using apredetermined first restoration network and a predetermined secondrestoration network comprises step of: restoring the detail layer byusing the residual network and restoring the basic layer by using theU-Net network.
 6. The method according to claim 5, wherein the detaillayer contains high frequency components and compression artifacts ofthe low dynamic range image, and the basic layer contains low frequencycomponents of the low dynamic range image; the step of restoring thedetail layer by using the residual network and restoring the basic layerby using the U-Net network comprises step of: restoring the highfrequency components by using the residual network to remove thecompression artifacts, and restoring the low frequency components byusing U-Net network.
 7. The method according to claim 6, wherein thehigh frequency components comprise edges and contours, and the lowfrequency components comprise color information and structuralinformation.
 8. The method according to claim 1, wherein the fusionnetwork uses a residual network.
 9. An electronic device, comprising: astorage device, a processor, and a computer program stored on the memoryand executable on the processor, wherein when the processor executes theprogram the processor implements steps as following: acquiring one ormore low dynamic range images; performing a decomposition operation tothe one or more low dynamic range images to acquire a detail layer and abasic layer of the low dynamic range image; restoring the detail layerand the basic layer by using a predetermined first restoration networkand a predetermined second restoration network to acquire restoreddetail layer and basic layer, respectively; and adjusting the restoreddetail layer and basic layer by using a predetermined fusion network toacquire an adjusted high dynamic range image; wherein the firstrestoration network is a residual network and the second firstrestoration network is a U-Net network; wherein the residual networkcomprises one or more convolution layers on both sides and multipleresidual blocks in the middle, and each of the residual blocks containsa first convolution layer, a second activation layer, and a thirdconvolution layer and a fourth activation layer arranged in sequence;and before the fourth activation layer further comprises: performing anaddition operation on an input image of the residual block and an outputimage of the third convolution layer; and wherein the U-Net networkcomprises multiple convolution blocks and deconvolution blocks, themultiple convolution blocks are located in front of the multipledeconvolution blocks, and each of the multiple convolution blockcomprises a convolution layer, an activation layer, and a convolutionlayer and an activation layer arranged in sequence, and in each of themultiple deconvolution blocks, first up-sampling to expand theresolution of a feature map, and then performing a convolutionoperation; each of the multiple deconvolution blocks contains anup-sampling layer and a convolution layer and an activation layerarranged in sequence.